The use of multiple Bloom-filters to minimize data transfer during distributed query processing.
Bibliographic record
Abstract
Query processing in distributed database system requires the transmission of data between computers in network. Therefore, query optimization in distributed database system is an important research issue. Since employing the optimization for general query is NP-Hard, heuristics are applied to find a cost-effective and efficient processing strategy. The challenge is how to efficiently minimize either transmission time or local processing cost in a query process. In the thesis, we propose a new reduction approach to significantly minimize data transmission time. The algorithm [32] is used to process general queries by simply substituting single Bloom filter that is based on perfect hashing in the reduction approach [32] with multiple Bloom filters which are based on non-perfect hash functions. Our approach aims to minimize data transmission time. The evaluation of our application is against the reducer [32]. An analysis of how the number of Bloom-filters affects the performance of the algorithm [32] is provided in the thesis. An amount of experimental results will be used to evaluate the performance of our reduction approach. Compared to the approach in paper [32], our reduction approach provides a more practical, cost effective and efficient processing query solution. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2000 .W36. Source: Masters Abstracts International, Volume: 40-03, page: 0729. Thesis (M.Sc.)--University of Windsor (Canada), 2001.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".